Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Comput Intell Neurosci. 2022 May 19;2022:6185013. doi: 10.1155/2022/6185013. eCollection 2022.
It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.
由于大流行在全球范围内造成了许多有害后果，因此建立一种可靠的方法来检测感染 COVID-19 的人至关重要。如果患者感染了 COVID-19，可以使用胸部 X 光片来确定这一点。在这项工作中，我们训练的胶囊神经网络模型可以对显示 COVID-19 感染的 X 射线进行分类。我们使用 6310 张胸部 X 射线照片来训练模型，将其分为三类：正常、肺炎和 COVID-19。这项工作被认为是一种通过 X 射线图像对 COVID-19 疾病进行分类的改进的深度学习模型。与经典卷积神经网络（CNN）模型相比，CapsNet 的优势在于不变性视角、更少的参数和更好的泛化能力。在模型训练过程中，所提出的模型达到了超过 95%的准确率，优于其他最先进的算法。此外，为了帮助在胸部 X 光片中检测 COVID-19，该模型可以提供额外的信息。
